Overview

Dataset statistics

Number of variables14
Number of observations12169
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory120.0 B

Variable types

Text2
Numeric12

Alerts

percentage0to15years is highly overall correlated with percentagehouseholdswithchildrenHigh correlation
percentage25to45years is highly overall correlated with percentage45to65years and 4 other fieldsHigh correlation
percentage45to65years is highly overall correlated with percentage25to45years and 1 other fieldsHigh correlation
percentage65yearsorolder is highly overall correlated with percentage25to45yearsHigh correlation
percentagehouseholdswithchildren is highly overall correlated with percentage0to15years and 1 other fieldsHigh correlation
percentagehouseholdswithoutchildren is highly overall correlated with percentage25to45years and 2 other fieldsHigh correlation
percentagenonwesternmigrationbackground is highly overall correlated with percentage25to45years and 4 other fieldsHigh correlation
percentageonepersonhouseholds is highly overall correlated with percentagehouseholdswithchildren and 3 other fieldsHigh correlation
percentagewesternmigrationbackground is highly overall correlated with percentagenonwesternmigrationbackground and 1 other fieldsHigh correlation
populationdensityperkm2 is highly overall correlated with percentage25to45years and 2 other fieldsHigh correlation
neighborhoodcode has unique valuesUnique
percentagenonwesternmigrationbackground has 1398 (11.5%) zerosZeros

Reproduction

Analysis started2024-07-05 09:49:29.331563
Analysis finished2024-07-05 09:49:54.429213
Duration25.1 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

neighborhoodcode
Text

UNIQUE 

Distinct12169
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
2024-07-05T11:49:54.595948image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters121690
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12169 ?
Unique (%)100.0%

Sample

1st rowBU00030000
2nd rowBU00030001
3rd rowBU00030002
4th rowBU00030007
5th rowBU00030008
ValueCountFrequency (%)
bu00030000 1
 
< 0.1%
bu00100204 1
 
< 0.1%
bu00140803 1
 
< 0.1%
bu00100306 1
 
< 0.1%
bu00030002 1
 
< 0.1%
bu00030007 1
 
< 0.1%
bu00030008 1
 
< 0.1%
bu00030009 1
 
< 0.1%
bu00100101 1
 
< 0.1%
bu00100202 1
 
< 0.1%
Other values (12159) 12159
99.9%
2024-07-05T11:49:55.028823image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 35264
29.0%
1 13917
 
11.4%
B 12169
 
10.0%
U 12169
 
10.0%
2 7541
 
6.2%
3 7385
 
6.1%
9 6383
 
5.2%
4 5923
 
4.9%
5 5639
 
4.6%
6 5221
 
4.3%
Other values (2) 10079
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 97352
80.0%
Uppercase Letter 24338
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35264
36.2%
1 13917
 
14.3%
2 7541
 
7.7%
3 7385
 
7.6%
9 6383
 
6.6%
4 5923
 
6.1%
5 5639
 
5.8%
6 5221
 
5.4%
7 5095
 
5.2%
8 4984
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
B 12169
50.0%
U 12169
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 97352
80.0%
Latin 24338
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35264
36.2%
1 13917
 
14.3%
2 7541
 
7.7%
3 7385
 
7.6%
9 6383
 
6.6%
4 5923
 
6.1%
5 5639
 
5.8%
6 5221
 
5.4%
7 5095
 
5.2%
8 4984
 
5.1%
Latin
ValueCountFrequency (%)
B 12169
50.0%
U 12169
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121690
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35264
29.0%
1 13917
 
11.4%
B 12169
 
10.0%
U 12169
 
10.0%
2 7541
 
6.2%
3 7385
 
6.1%
9 6383
 
5.2%
4 5923
 
4.9%
5 5639
 
4.6%
6 5221
 
4.3%
Other values (2) 10079
 
8.3%
Distinct11319
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
2024-07-05T11:49:55.345595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length60
Median length50
Mean length15.79004
Min length2

Characters and Unicode

Total characters192149
Distinct characters81
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10896 ?
Unique (%)89.5%

Sample

1st rowAppingedam-Centrum
2nd rowAppingedam-West
3rd rowAppingedam-Oost
4th rowVerspreide huizen Damsterdiep en Eemskanaal
5th rowVerspreide huizen ten zuiden van Eemskanaal
ValueCountFrequency (%)
verspreide 1356
 
6.1%
huizen 1350
 
6.1%
de 775
 
3.5%
en 630
 
2.8%
buitengebied 427
 
1.9%
omgeving 250
 
1.1%
noord 231
 
1.0%
kern 211
 
1.0%
zuid 204
 
0.9%
west 177
 
0.8%
Other values (9477) 16556
74.7%
2024-07-05T11:49:55.873308image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 30756
16.0%
r 14949
 
7.8%
n 13145
 
6.8%
i 11579
 
6.0%
9998
 
5.2%
o 9759
 
5.1%
t 8853
 
4.6%
d 8827
 
4.6%
u 8449
 
4.4%
s 7776
 
4.0%
Other values (71) 68058
35.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 158184
82.3%
Uppercase Letter 20342
 
10.6%
Space Separator 9998
 
5.2%
Dash Punctuation 2349
 
1.2%
Other Punctuation 593
 
0.3%
Decimal Number 480
 
0.2%
Close Punctuation 100
 
0.1%
Open Punctuation 100
 
0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 30756
19.4%
r 14949
9.5%
n 13145
 
8.3%
i 11579
 
7.3%
o 9759
 
6.2%
t 8853
 
5.6%
d 8827
 
5.6%
u 8449
 
5.3%
s 7776
 
4.9%
a 7665
 
4.8%
Other values (25) 36426
23.0%
Uppercase Letter
ValueCountFrequency (%)
B 2247
 
11.0%
V 2154
 
10.6%
H 1429
 
7.0%
D 1418
 
7.0%
W 1362
 
6.7%
O 1261
 
6.2%
S 1203
 
5.9%
N 1163
 
5.7%
Z 1071
 
5.3%
K 1003
 
4.9%
Other values (15) 6031
29.6%
Decimal Number
ValueCountFrequency (%)
1 115
24.0%
2 97
20.2%
0 96
20.0%
3 87
18.1%
4 34
 
7.1%
5 21
 
4.4%
6 8
 
1.7%
9 8
 
1.7%
7 7
 
1.5%
8 7
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 262
44.2%
' 140
23.6%
, 106
17.9%
/ 77
 
13.0%
" 6
 
1.0%
& 2
 
0.3%
Space Separator
ValueCountFrequency (%)
9998
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2349
100.0%
Close Punctuation
ValueCountFrequency (%)
) 100
100.0%
Open Punctuation
ValueCountFrequency (%)
( 100
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 178526
92.9%
Common 13623
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 30756
17.2%
r 14949
 
8.4%
n 13145
 
7.4%
i 11579
 
6.5%
o 9759
 
5.5%
t 8853
 
5.0%
d 8827
 
4.9%
u 8449
 
4.7%
s 7776
 
4.4%
a 7665
 
4.3%
Other values (50) 56768
31.8%
Common
ValueCountFrequency (%)
9998
73.4%
- 2349
 
17.2%
. 262
 
1.9%
' 140
 
1.0%
1 115
 
0.8%
, 106
 
0.8%
) 100
 
0.7%
( 100
 
0.7%
2 97
 
0.7%
0 96
 
0.7%
Other values (11) 260
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 192076
> 99.9%
None 73
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 30756
16.0%
r 14949
 
7.8%
n 13145
 
6.8%
i 11579
 
6.0%
9998
 
5.2%
o 9759
 
5.1%
t 8853
 
4.6%
d 8827
 
4.6%
u 8449
 
4.4%
s 7776
 
4.0%
Other values (62) 67985
35.4%
None
ValueCountFrequency (%)
ë 31
42.5%
â 20
27.4%
û 6
 
8.2%
é 6
 
8.2%
ö 4
 
5.5%
ï 3
 
4.1%
ú 1
 
1.4%
ô 1
 
1.4%
á 1
 
1.4%

populationdensityperkm2
Real number (ℝ)

HIGH CORRELATION 

Distinct5949
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3285.2184
Minimum2
Maximum54220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size190.1 KiB
2024-07-05T11:49:56.094955image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile19
Q1161
median2038
Q35126
95-th percentile10267.4
Maximum54220
Range54218
Interquartile range (IQR)4965

Descriptive statistics

Standard deviation4017.5603
Coefficient of variation (CV)1.2229203
Kurtosis10.105902
Mean3285.2184
Median Absolute Deviation (MAD)1979
Skewness2.3992183
Sum39977823
Variance16140791
MonotonicityNot monotonic
2024-07-05T11:49:56.795748image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 64
 
0.5%
32 54
 
0.4%
19 54
 
0.4%
16 54
 
0.4%
21 53
 
0.4%
13 53
 
0.4%
25 49
 
0.4%
18 48
 
0.4%
34 47
 
0.4%
10 47
 
0.4%
Other values (5939) 11646
95.7%
ValueCountFrequency (%)
2 2
 
< 0.1%
3 8
 
0.1%
4 6
 
< 0.1%
5 20
0.2%
6 17
 
0.1%
7 27
0.2%
8 33
0.3%
9 38
0.3%
10 47
0.4%
11 40
0.3%
ValueCountFrequency (%)
54220 1
< 0.1%
36296 1
< 0.1%
35855 1
< 0.1%
34263 1
< 0.1%
33037 1
< 0.1%
32381 1
< 0.1%
31931 1
< 0.1%
31907 1
< 0.1%
30952 1
< 0.1%
29872 1
< 0.1%

percentage0to15years
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.088914
Minimum0
Maximum49
Zeros47
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size190.1 KiB
2024-07-05T11:49:56.977844image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q112
median15
Q318
95-th percentile24
Maximum49
Range49
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.1720227
Coefficient of variation (CV)0.3427697
Kurtosis1.7316079
Mean15.088914
Median Absolute Deviation (MAD)3
Skewness0.3419234
Sum183617
Variance26.749819
MonotonicityNot monotonic
2024-07-05T11:49:57.173128image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
15 1221
 
10.0%
16 1204
 
9.9%
14 1123
 
9.2%
17 1020
 
8.4%
13 966
 
7.9%
18 830
 
6.8%
12 812
 
6.7%
19 636
 
5.2%
11 633
 
5.2%
20 480
 
3.9%
Other values (33) 3244
26.7%
ValueCountFrequency (%)
0 47
 
0.4%
1 51
 
0.4%
2 60
 
0.5%
3 57
 
0.5%
4 97
 
0.8%
5 135
1.1%
6 147
1.2%
7 204
1.7%
8 274
2.3%
9 325
2.7%
ValueCountFrequency (%)
49 1
 
< 0.1%
41 2
 
< 0.1%
40 1
 
< 0.1%
39 1
 
< 0.1%
38 3
 
< 0.1%
37 6
 
< 0.1%
36 8
 
0.1%
35 12
0.1%
34 11
0.1%
33 22
0.2%

percentage15to25years
Real number (ℝ)

Distinct74
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.455255
Minimum0
Maximum96
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size190.1 KiB
2024-07-05T11:49:57.420637image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median12
Q314
95-th percentile19
Maximum96
Range96
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.3004401
Coefficient of variation (CV)0.42555853
Kurtosis41.161695
Mean12.455255
Median Absolute Deviation (MAD)2
Skewness4.6311765
Sum151568
Variance28.094665
MonotonicityNot monotonic
2024-07-05T11:49:57.620659image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 1678
13.8%
12 1643
13.5%
10 1559
12.8%
13 1291
10.6%
9 1047
8.6%
14 1025
8.4%
15 716
 
5.9%
8 579
 
4.8%
16 469
 
3.9%
7 347
 
2.9%
Other values (64) 1815
14.9%
ValueCountFrequency (%)
0 15
 
0.1%
1 10
 
0.1%
2 28
 
0.2%
3 31
 
0.3%
4 82
 
0.7%
5 128
 
1.1%
6 194
 
1.6%
7 347
 
2.9%
8 579
4.8%
9 1047
8.6%
ValueCountFrequency (%)
96 1
< 0.1%
84 1
< 0.1%
79 1
< 0.1%
78 1
< 0.1%
77 1
< 0.1%
76 1
< 0.1%
73 1
< 0.1%
72 1
< 0.1%
71 2
< 0.1%
69 2
< 0.1%

percentage25to45years
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.112006
Minimum0
Maximum73
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size190.1 KiB
2024-07-05T11:49:57.794078image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q117
median21
Q325
95-th percentile38
Maximum73
Range73
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.085203
Coefficient of variation (CV)0.36564765
Kurtosis3.6855561
Mean22.112006
Median Absolute Deviation (MAD)4
Skewness1.3923853
Sum269081
Variance65.370508
MonotonicityNot monotonic
2024-07-05T11:49:58.011103image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 928
 
7.6%
21 841
 
6.9%
19 821
 
6.7%
22 817
 
6.7%
18 760
 
6.2%
17 733
 
6.0%
23 717
 
5.9%
16 604
 
5.0%
24 600
 
4.9%
15 485
 
4.0%
Other values (62) 4863
40.0%
ValueCountFrequency (%)
0 6
 
< 0.1%
1 6
 
< 0.1%
2 8
 
0.1%
3 6
 
< 0.1%
4 14
 
0.1%
5 19
 
0.2%
6 21
 
0.2%
7 42
0.3%
8 52
0.4%
9 90
0.7%
ValueCountFrequency (%)
73 1
 
< 0.1%
71 1
 
< 0.1%
70 4
< 0.1%
69 2
< 0.1%
68 2
< 0.1%
67 2
< 0.1%
66 2
< 0.1%
64 2
< 0.1%
63 1
 
< 0.1%
62 3
< 0.1%

percentage45to65years
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.196729
Minimum0
Maximum71
Zeros9
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size190.1 KiB
2024-07-05T11:49:58.193743image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q126
median30
Q334
95-th percentile41
Maximum71
Range71
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.736219
Coefficient of variation (CV)0.22307777
Kurtosis1.6488873
Mean30.196729
Median Absolute Deviation (MAD)4
Skewness-0.32376216
Sum367464
Variance45.376646
MonotonicityNot monotonic
2024-07-05T11:49:58.427672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 820
 
6.7%
29 818
 
6.7%
31 782
 
6.4%
28 772
 
6.3%
33 715
 
5.9%
27 707
 
5.8%
32 704
 
5.8%
34 666
 
5.5%
26 633
 
5.2%
35 573
 
4.7%
Other values (52) 4979
40.9%
ValueCountFrequency (%)
0 9
0.1%
1 14
0.1%
2 7
0.1%
3 9
0.1%
4 9
0.1%
5 5
 
< 0.1%
6 6
< 0.1%
7 7
0.1%
8 9
0.1%
9 12
0.1%
ValueCountFrequency (%)
71 1
 
< 0.1%
65 1
 
< 0.1%
61 1
 
< 0.1%
58 1
 
< 0.1%
57 2
 
< 0.1%
56 2
 
< 0.1%
55 4
< 0.1%
54 3
< 0.1%
53 3
< 0.1%
52 6
< 0.1%

percentage65yearsorolder
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.163366
Minimum0
Maximum99
Zeros46
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size190.1 KiB
2024-07-05T11:49:58.677171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q114
median19
Q324
95-th percentile36
Maximum99
Range99
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.2999952
Coefficient of variation (CV)0.46123228
Kurtosis6.4594124
Mean20.163366
Median Absolute Deviation (MAD)5
Skewness1.4999752
Sum245368
Variance86.48991
MonotonicityNot monotonic
2024-07-05T11:49:58.877696image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 722
 
5.9%
19 674
 
5.5%
18 668
 
5.5%
17 664
 
5.5%
21 653
 
5.4%
22 634
 
5.2%
16 610
 
5.0%
23 529
 
4.3%
14 507
 
4.2%
15 501
 
4.1%
Other values (77) 6007
49.4%
ValueCountFrequency (%)
0 46
 
0.4%
1 38
 
0.3%
2 47
 
0.4%
3 62
 
0.5%
4 84
 
0.7%
5 103
0.8%
6 140
1.2%
7 153
1.3%
8 201
1.7%
9 228
1.9%
ValueCountFrequency (%)
99 1
< 0.1%
98 1
< 0.1%
97 1
< 0.1%
95 1
< 0.1%
90 2
< 0.1%
88 2
< 0.1%
86 2
< 0.1%
85 1
< 0.1%
83 2
< 0.1%
81 1
< 0.1%

percentageonepersonhouseholds
Real number (ℝ)

HIGH CORRELATION 

Distinct124
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.080319
Minimum0
Maximum100
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size190.1 KiB
2024-07-05T11:49:59.074132image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q122
median29
Q339
95-th percentile61
Maximum100
Range100
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.658775
Coefficient of variation (CV)0.45693981
Kurtosis1.5495791
Mean32.080319
Median Absolute Deviation (MAD)8
Skewness1.111363
Sum390385.4
Variance214.87968
MonotonicityNot monotonic
2024-07-05T11:49:59.270158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 454
 
3.7%
24 451
 
3.7%
21 437
 
3.6%
28 436
 
3.6%
29 432
 
3.6%
23 414
 
3.4%
25 414
 
3.4%
27 401
 
3.3%
22 395
 
3.2%
20 377
 
3.1%
Other values (114) 7958
65.4%
ValueCountFrequency (%)
0 8
 
0.1%
1 1
 
< 0.1%
2 4
 
< 0.1%
3 5
 
< 0.1%
4 7
 
0.1%
5 18
 
0.1%
6 15
 
0.1%
7 28
0.2%
8 28
0.2%
9 45
0.4%
ValueCountFrequency (%)
100 2
 
< 0.1%
99 5
< 0.1%
98 2
 
< 0.1%
97 1
 
< 0.1%
96 5
< 0.1%
95 4
< 0.1%
94 6
< 0.1%
93 5
< 0.1%
92 5
< 0.1%
91 5
< 0.1%

percentagehouseholdswithoutchildren
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.219492
Minimum0
Maximum80
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size190.1 KiB
2024-07-05T11:49:59.462993image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q127
median32
Q337
95-th percentile46
Maximum80
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.3793598
Coefficient of variation (CV)0.26007113
Kurtosis0.8743225
Mean32.219492
Median Absolute Deviation (MAD)5
Skewness0.10709112
Sum392079
Variance70.21367
MonotonicityNot monotonic
2024-07-05T11:49:59.726814image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 672
 
5.5%
34 655
 
5.4%
35 639
 
5.3%
32 629
 
5.2%
30 609
 
5.0%
31 602
 
4.9%
36 559
 
4.6%
37 506
 
4.2%
38 500
 
4.1%
28 499
 
4.1%
Other values (82) 6299
51.8%
ValueCountFrequency (%)
0 8
0.1%
1 3
 
< 0.1%
2 7
0.1%
3 5
< 0.1%
4 4
 
< 0.1%
5 12
0.1%
6 11
0.1%
7 11
0.1%
8 5
< 0.1%
9 10
0.1%
ValueCountFrequency (%)
80 1
 
< 0.1%
70 1
 
< 0.1%
68 1
 
< 0.1%
66 1
 
< 0.1%
65 3
< 0.1%
64 2
 
< 0.1%
63 4
< 0.1%
62 6
< 0.1%
61 5
< 0.1%
60 7
0.1%

percentagehouseholdswithchildren
Real number (ℝ)

HIGH CORRELATION 

Distinct104
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.711381
Minimum0
Maximum94
Zeros24
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size190.1 KiB
2024-07-05T11:49:59.927433image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q129
median36
Q343
95-th percentile55
Maximum94
Range94
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.900585
Coefficient of variation (CV)0.33324349
Kurtosis0.62312862
Mean35.711381
Median Absolute Deviation (MAD)7
Skewness-0.14019143
Sum434571.8
Variance141.62393
MonotonicityNot monotonic
2024-07-05T11:50:00.126792image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 547
 
4.5%
35 509
 
4.2%
36 503
 
4.1%
37 479
 
3.9%
39 474
 
3.9%
33 472
 
3.9%
40 461
 
3.8%
34 459
 
3.8%
41 452
 
3.7%
32 382
 
3.1%
Other values (94) 7431
61.1%
ValueCountFrequency (%)
0 24
0.2%
1 22
0.2%
2 22
0.2%
3 22
0.2%
4 24
0.2%
5 36
0.3%
6 43
0.4%
7 41
0.3%
8 37
0.3%
9 53
0.4%
ValueCountFrequency (%)
94 1
 
< 0.1%
80 2
 
< 0.1%
79 1
 
< 0.1%
78 2
 
< 0.1%
77 3
< 0.1%
76 1
 
< 0.1%
75 2
 
< 0.1%
74 7
0.1%
73 4
< 0.1%
72 6
< 0.1%

percentagewesternmigrationbackground
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9004027
Minimum0
Maximum85
Zeros111
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size190.1 KiB
2024-07-05T11:50:00.309976image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q311
95-th percentile20
Maximum85
Range85
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.3797562
Coefficient of variation (CV)0.71679411
Kurtosis14.216781
Mean8.9004027
Median Absolute Deviation (MAD)3
Skewness2.6394388
Sum108309
Variance40.701289
MonotonicityNot monotonic
2024-07-05T11:50:00.503559image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 1091
 
9.0%
4 1047
 
8.6%
5 1036
 
8.5%
8 1025
 
8.4%
6 996
 
8.2%
9 919
 
7.6%
10 831
 
6.8%
3 828
 
6.8%
11 677
 
5.6%
2 525
 
4.3%
Other values (55) 3194
26.2%
ValueCountFrequency (%)
0 111
 
0.9%
1 261
 
2.1%
2 525
4.3%
3 828
6.8%
4 1047
8.6%
5 1036
8.5%
6 996
8.2%
7 1091
9.0%
8 1025
8.4%
9 919
7.6%
ValueCountFrequency (%)
85 1
 
< 0.1%
77 1
 
< 0.1%
71 3
< 0.1%
68 1
 
< 0.1%
67 1
 
< 0.1%
63 2
< 0.1%
62 1
 
< 0.1%
61 1
 
< 0.1%
60 3
< 0.1%
59 2
< 0.1%

percentagenonwesternmigrationbackground
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2109458
Minimum0
Maximum99
Zeros1398
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size190.1 KiB
2024-07-05T11:50:00.692943image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q310
95-th percentile32
Maximum99
Range99
Interquartile range (IQR)8

Descriptive statistics

Standard deviation11.491856
Coefficient of variation (CV)1.3995776
Kurtosis10.050509
Mean8.2109458
Median Absolute Deviation (MAD)3
Skewness2.8221187
Sum99919
Variance132.06275
MonotonicityNot monotonic
2024-07-05T11:50:00.943647image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1643
13.5%
2 1466
12.0%
0 1398
11.5%
3 1178
 
9.7%
4 964
 
7.9%
5 637
 
5.2%
6 555
 
4.6%
7 450
 
3.7%
8 357
 
2.9%
9 319
 
2.6%
Other values (81) 3202
26.3%
ValueCountFrequency (%)
0 1398
11.5%
1 1643
13.5%
2 1466
12.0%
3 1178
9.7%
4 964
7.9%
5 637
 
5.2%
6 555
 
4.6%
7 450
 
3.7%
8 357
 
2.9%
9 319
 
2.6%
ValueCountFrequency (%)
99 1
 
< 0.1%
97 1
 
< 0.1%
92 1
 
< 0.1%
91 1
 
< 0.1%
89 1
 
< 0.1%
88 1
 
< 0.1%
87 1
 
< 0.1%
86 1
 
< 0.1%
82 2
< 0.1%
81 3
< 0.1%

percentagemen
Real number (ℝ)

Distinct297
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.589613
Minimum25.4
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size190.1 KiB
2024-07-05T11:50:01.143215image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum25.4
5-th percentile45.9
Q148.8
median50.2
Q352
95-th percentile56.2
Maximum100
Range74.6
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation3.7469408
Coefficient of variation (CV)0.074065417
Kurtosis26.319051
Mean50.589613
Median Absolute Deviation (MAD)1.6
Skewness2.6000205
Sum615625
Variance14.039565
MonotonicityNot monotonic
2024-07-05T11:50:01.326493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 917
 
7.5%
50.5 203
 
1.7%
49.4 202
 
1.7%
50.7 196
 
1.6%
49.2 194
 
1.6%
49.3 192
 
1.6%
50.6 187
 
1.5%
49.5 186
 
1.5%
50.3 183
 
1.5%
50.8 179
 
1.5%
Other values (287) 9530
78.3%
ValueCountFrequency (%)
25.4 1
< 0.1%
27.5 1
< 0.1%
29.4 2
< 0.1%
30.3 1
< 0.1%
30.8 1
< 0.1%
32 1
< 0.1%
32.4 1
< 0.1%
33.3 1
< 0.1%
34.2 1
< 0.1%
34.9 1
< 0.1%
ValueCountFrequency (%)
100 4
< 0.1%
96 1
 
< 0.1%
91.7 2
< 0.1%
91.3 2
< 0.1%
90.9 1
 
< 0.1%
87.8 1
 
< 0.1%
87.5 1
 
< 0.1%
86.2 1
 
< 0.1%
85.7 1
 
< 0.1%
82.9 1
 
< 0.1%

Interactions

2024-07-05T11:49:52.029652image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:31.262118image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:33.129189image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:34.871870image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:36.944534image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:38.741282image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:40.550657image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:42.407261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:44.095002image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:46.764796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:48.498122image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:50.163729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:52.180637image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:31.420290image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:33.270454image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:35.034597image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:37.091709image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:38.879381image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:40.683550image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:42.548370image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:44.303646image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:46.897883image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:48.630689image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:50.364388image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:52.312741image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:31.562869image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:33.403586image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:35.172652image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:37.253740image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:39.026510image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:40.833134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:42.666420image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:44.485212image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:47.044333image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:48.747995image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:50.593399image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:52.453612image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:31.710057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:33.529573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:35.316026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:37.403622image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:39.164579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:40.983810image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:42.799365image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:44.699672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:47.197950image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:48.894304image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:50.725990image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:52.662906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:31.863616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:33.683306image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:35.456842image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:37.562953image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:39.337849image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:41.210929image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:42.949294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:44.891023image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:47.354023image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:49.064006image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:50.863474image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:52.813312image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:32.010808image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:33.838769image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:35.619599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:37.708110image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:39.517118image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:41.422859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:43.099241image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:45.165807image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:47.520562image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:49.213597image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:50.997666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:52.946171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:32.157934image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:33.978447image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:35.757674image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:37.846205image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:39.684243image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:41.549787image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:43.253979image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:45.731685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:47.689661image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:49.337296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:51.146650image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:53.079756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:32.364686image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:34.117484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:35.904800image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:37.993345image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:39.818080image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:41.683676image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:43.394261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:45.904037image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:47.831774image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:49.470223image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:51.284386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:53.229244image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:32.527400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:34.270411image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:36.058441image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:38.150052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:39.984052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:41.850386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:43.532473image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:46.084171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:47.982714image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:49.617035image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:51.496777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:53.370335image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:32.681127image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:34.403611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:36.220641image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:38.286906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:40.116808image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:41.981867image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:43.666325image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:46.259377image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:48.097569image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:49.748837image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:51.629721image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:53.505019image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:32.830257image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:34.540274image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:36.353881image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:38.436924image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:40.253684image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:42.116328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:43.799593image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:46.432318image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:48.216044image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:49.880663image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:51.762989image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:53.646138image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:32.966357image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:34.687045image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:36.490201image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:38.594129image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:40.404138image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:42.253696image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:43.932887image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:46.564756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:48.353966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:50.013560image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:51.896332image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-07-05T11:50:01.492801image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
percentage0to15yearspercentage15to25yearspercentage25to45yearspercentage45to65yearspercentage65yearsorolderpercentagehouseholdswithchildrenpercentagehouseholdswithoutchildrenpercentagemenpercentagenonwesternmigrationbackgroundpercentageonepersonhouseholdspercentagewesternmigrationbackgroundpopulationdensityperkm2
percentage0to15years1.0000.0360.268-0.224-0.4550.662-0.239-0.0150.068-0.360-0.2020.101
percentage15to25years0.0361.000-0.0500.135-0.4620.339-0.2190.266-0.061-0.173-0.167-0.112
percentage25to45years0.268-0.0501.000-0.592-0.535-0.100-0.595-0.0260.5730.4020.3530.531
percentage45to65years-0.2240.135-0.5921.0000.0320.2540.4840.333-0.496-0.458-0.271-0.502
percentage65yearsorolder-0.455-0.462-0.5350.0321.000-0.4560.484-0.296-0.1990.144-0.016-0.120
percentagehouseholdswithchildren0.6620.339-0.1000.254-0.4561.0000.0120.209-0.279-0.788-0.432-0.250
percentagehouseholdswithoutchildren-0.239-0.219-0.5950.4840.4840.0121.0000.136-0.549-0.530-0.345-0.478
percentagemen-0.0150.266-0.0260.333-0.2960.2090.1361.000-0.316-0.270-0.199-0.432
percentagenonwesternmigrationbackground0.068-0.0610.573-0.496-0.199-0.279-0.549-0.3161.0000.5340.5810.724
percentageonepersonhouseholds-0.360-0.1730.402-0.4580.144-0.788-0.530-0.2700.5341.0000.5310.478
percentagewesternmigrationbackground-0.202-0.1670.353-0.271-0.016-0.432-0.345-0.1990.5810.5311.0000.471
populationdensityperkm20.101-0.1120.531-0.502-0.120-0.250-0.478-0.4320.7240.4780.4711.000

Missing values

2024-07-05T11:49:53.929095image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-05T11:49:54.245604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

neighborhoodcodeneighborhoodnamepopulationdensityperkm2percentage0to15yearspercentage15to25yearspercentage25to45yearspercentage45to65yearspercentage65yearsorolderpercentageonepersonhouseholdspercentagehouseholdswithoutchildrenpercentagehouseholdswithchildrenpercentagewesternmigrationbackgroundpercentagenonwesternmigrationbackgroundpercentagemen
0BU00030000Appingedam-Centrum2812.010.09.021.030.029.054.027.019.06.04.047.9
1BU00030001Appingedam-West1922.016.012.019.033.021.028.037.036.05.04.049.6
2BU00030002Appingedam-Oost2012.016.010.021.027.026.037.030.033.09.011.048.2
3BU00030007Verspreide huizen Damsterdiep en Eemskanaal60.020.014.020.036.010.016.030.053.07.02.053.8
4BU00030008Verspreide huizen ten zuiden van Eemskanaal18.013.014.012.045.017.015.043.043.03.01.055.0
5BU00030009Verspreide huizen ten noorden van het Damsterdiep22.018.014.014.037.017.020.037.043.05.02.050.0
6BU00100101Centrum2899.03.08.022.025.042.068.025.07.010.011.052.8
7BU00100202Over de Gracht4650.014.010.021.032.024.029.037.034.08.02.050.7
8BU00100203Scheepvaartbuurt2455.08.07.016.019.050.055.029.016.08.05.043.5
9BU00100204Steenbakkersbuurt2707.016.011.021.029.023.028.036.036.04.05.050.5
neighborhoodcodeneighborhoodnamepopulationdensityperkm2percentage0to15yearspercentage15to25yearspercentage25to45yearspercentage45to65yearspercentage65yearsorolderpercentageonepersonhouseholdspercentagehouseholdswithoutchildrenpercentagehouseholdswithchildrenpercentagewesternmigrationbackgroundpercentagenonwesternmigrationbackgroundpercentagemen
13580BU19781605Oud-Alblas-Buitengebied-Noord16.025.021.017.028.08.013.013.074.03.02.050.0
13581BU19781701Schelluinen-Dorp3046.018.011.024.029.017.023.038.040.07.04.049.8
13585BU19781705Schelluinen-Buitengebied-Noordoost68.014.015.014.036.021.023.032.045.05.02.052.9
13586BU19781801Dijkgebied-Streefkerk541.018.022.017.028.015.017.037.046.02.00.052.0
13587BU19781802Streefkerk-Buitengebied15.019.026.017.033.06.014.025.061.01.01.054.8
13588BU19781803Streefkerk-Dorp3557.017.015.019.028.022.026.034.040.03.02.050.1
13589BU19781901Waal-Dorp2043.017.017.023.035.07.021.024.055.03.02.054.5
13591BU19782002Kern-Dorp3870.014.016.018.030.022.026.037.037.02.00.051.1
13592BU19782003Lintbebouwing-Oost536.025.013.013.025.023.010.033.057.01.05.046.7
13593BU19782004Lintbebouwing-West616.019.013.015.030.023.013.037.050.00.00.052.2